102 lines
3.4 KiB
TypeScript
102 lines
3.4 KiB
TypeScript
import { INodeData } from '../../src'
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import { VectorStore } from '@langchain/core/vectorstores'
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export const resolveVectorStoreOrRetriever = (
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nodeData: INodeData,
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vectorStore: VectorStore,
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metadataFilter?: string | object | undefined
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) => {
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const output = nodeData.outputs?.output as string
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const searchType = nodeData.outputs?.searchType as string
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const topK = nodeData.inputs?.topK as string
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const k = topK ? parseFloat(topK) : 4
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// If it is already pre-defined in lc_kwargs, then don't pass it again
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const filter = vectorStore?.lc_kwargs?.filter ? undefined : metadataFilter
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if (output === 'retriever') {
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if ('mmr' === searchType) {
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const fetchK = nodeData.inputs?.fetchK as string
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const lambda = nodeData.inputs?.lambda as string
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const f = fetchK ? parseInt(fetchK) : 20
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const l = lambda ? parseFloat(lambda) : 0.5
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return vectorStore.asRetriever({
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searchType: 'mmr',
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k: k,
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filter,
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searchKwargs: {
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fetchK: f,
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lambda: l
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}
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})
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} else {
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// "searchType" is "similarity"
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return vectorStore.asRetriever(k, filter)
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}
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} else if (output === 'vectorStore') {
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;(vectorStore as any).k = k
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;(vectorStore as any).filter = filter
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return vectorStore
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}
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}
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export const addMMRInputParams = (inputs: any[]) => {
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const mmrInputParams = [
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{
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label: 'Search Type',
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name: 'searchType',
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type: 'options',
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default: 'similarity',
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options: [
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{
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label: 'Similarity',
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name: 'similarity'
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},
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{
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label: 'Max Marginal Relevance',
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name: 'mmr'
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}
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],
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additionalParams: true,
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optional: true
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},
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{
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label: 'Fetch K (for MMR Search)',
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name: 'fetchK',
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description: 'Number of initial documents to fetch for MMR reranking. Default to 20. Used only when the search type is MMR',
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placeholder: '20',
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type: 'number',
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additionalParams: true,
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optional: true
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},
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{
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label: 'Lambda (for MMR Search)',
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name: 'lambda',
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description:
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'Number between 0 and 1 that determines the degree of diversity among the results, where 0 corresponds to maximum diversity and 1 to minimum diversity. Used only when the search type is MMR',
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placeholder: '0.5',
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type: 'number',
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additionalParams: true,
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optional: true
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}
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]
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inputs.push(...mmrInputParams)
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}
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export const howToUseFileUpload = `
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**File Upload**
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This allows file upload on the chat. Uploaded files will be upserted on the fly to the vector store.
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**Note:**
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- You can only turn on file upload for one vector store at a time.
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- At least one Document Loader node should be connected to the document input.
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- Document Loader should be file types like PDF, DOCX, TXT, etc.
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**How it works**
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- Uploaded files will have the metadata updated with the chatId.
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- This will allow the file to be associated with the chatId.
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- When querying, metadata will be filtered by chatId to retrieve files associated with the chatId.
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`
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